Embodiments of the present technique relate generally to web mining, and more particularly to web mining and clustering techniques for generating online predictions and recommendations.
Evolution of the Internet has resulted in a proliferation of e-commerce, web services and web based information systems where users are often forced to choose from an overwhelming amount of web content. Much of the web content, however, lies buried in documents designed for human consumption, such as home pages or product catalogs. Extracting relevant data from the web content available online in a multitude of formats and making this web content dynamically available to the users remains a complex and relevant task. Therefore, to aid a user's decision-making process, it has become increasingly important to design effective and intuitive business intelligence tools such as web mining, clustering and recommender systems. These business intelligence tools enable identification of user preferences, thereby allowing businesses to offer the users personalized product, service and content offerings.
Accordingly, numerous web sites provide the users with dynamic recommendations for products and services, targeted banner advertising, and personalized link selections. Such personalized recommendations enhance web-browsing experience for the users, thereby increasing sales of products and services. Design and implementation of effective web mining, clustering and/or recommender systems, therefore, has become critical to the success of e-commerce websites. To that end, the web mining systems are designed to model web data and determine usage patterns for optimization of web architecture and marketing of content.
Typically, web-mining systems perform three basic functions, namely preprocessing, sequential pattern discovery and sequential pattern analysis. The sequential pattern discovery techniques attempt to identify inter-session patterns, whereas the sequential pattern analysis techniques attempt to predict visit patterns of future users and recommend web pages to the users based on the identified patterns. Conventional web mining techniques, however, require manual intervention of a domain expert for establishing a context corresponding to the web usage patterns of the users. Identification of the patterns, therefore, may be subjective and may include several assumptions while grouping the web usage patterns under a particular context. Moreover, the conventional web mining techniques do not compensate for inaccurate assumptions, thereby generating erroneous patterns. Further, with the continual addition of online users and content, the identified patterns may quickly become obsolete or invalid.
It may therefore be desirable to develop an objective web mining and clustering method and system that dynamically identify meaningful patterns from web usage data with minimum assumptions and/or bias. Further, there is a need for a system that provides relevant recommendations to the users based on the identified patterns. Additionally, it may be desirable to develop the system that allows scalability and ability to adapt to dynamic changes in online user base and content.